Update README.md

README.md

@@ -1,3 +1,277 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- zh
+base_model:
+- hfl/chinese-macbert-base
+pipeline_tag: text-generation
+tags:
+- csc
+- text-correct
+- chinses-spelling-correct
+- chinese-spelling-check
+- 中文拼写纠错
+- 文本纠错
+- macbert4csc
+---
+# macbert4csc_v1
+## 概述(macbert4csc_v1)
+ - macro-correct, 中文拼写纠错CSC测评(文本纠错), 权重使用
+ - 项目地址在[https://github.com/yongzhuo/macro-correct](https://github.com/yongzhuo/macro-correct)
+ - 本模型权重为macbert4csc_v1, 使用macbert4csc架构(pycorrector版本), 其特点是在BertForMaskedLM后新加一个分支用于错误检测任务(分类任务, 不交互);
+ - 训练时使用了MFT(动态mask 0.2的非错误tokens), 同时det_loss的权重为0.15;
+ - 推理时舍弃了macbert后面的部分(det-layer);
+ - 如何使用: 1.使用transformers调用; 2.使用[macro-correct](https://github.com/yongzhuo/macro-correct)项目调用; 详情见***三、调用(Usage)***;
+
+## 目录
+* [一、测评(Test)](#一、测评(Test))
+* [二、结论(Conclusion)](#二、结论(Conclusion))
+* [三、调用(Usage)](#三、调用(Usage))
+* [四、论文(Paper)](#四、论文(Paper))
+* [五、参考(Refer)](#五、参考(Refer))
+* [六、引用(Cite)](#六、引用(Cite))
+
+
+## 一、测评(Test)
+### 1.1 测评数据来源
+地址为[Macropodus/csc_eval_public](https://huggingface.co/datasets/Macropodus/csc_eval_public), 所有训练数据均来自公网或开源数据, 训练数据为1千万左右, 混淆词典较大;
+``` 
+1.gen_de3.json(5545): '的地得'纠错, 由人民日报/学习强国/chinese-poetry等高质量数据人工生成;
+2.lemon_v2.tet.json(1053): relm论文提出的数据, 多领域拼写纠错数据集(7个领域), ; 包括game(GAM), encyclopedia (ENC), contract (COT), medical care(MEC), car (CAR), novel (NOV), and news (NEW)等领域;
+3.acc_rmrb.tet.json(4636): 来自NER-199801(人民日报高质量语料);
+4.acc_xxqg.tet.json(5000): 来自学习强国网站的高质量语料;
+5.gen_passage.tet.json(10000): 源数据为qwen生成的好词好句, 由几乎所有的开源数据汇总的混淆词典生成;
+6.textproof.tet.json(1447): NLP竞赛数据, TextProofreadingCompetition;
+7.gen_xxqg.tet.json(5000): 源数据为学习强国网站的高质量语料, 由几乎所有的开源数据汇总的混淆词典生成;
+8.faspell.dev.json(1000): 视频字幕通过OCR后获取的数据集; 来自爱奇艺的论文faspell;
+9.lomo_tet.json(5000): 主要为音似中文拼写纠错数据集; 来自腾讯; 人工标注的数据集CSCD-NS;
+10.mcsc_tet.5000.json(5000): 医学拼写纠错; 来自腾讯医典APP的真实历史日志; 注意论文说该数据集只关注医学实体的纠错, 常用字等的纠错并不关注;
+11.ecspell.dev.json(1500): 来自ECSpell论文, 包括(law/med/gov)等三个领域;
+12.sighan2013.dev.json(1000): 来自sighan13会议;
+13.sighan2014.dev.json(1062): 来自sighan14会议;
+14.sighan2015.dev.json(1100): 来自sighan15会议;
+```
+### 1.2 测评数据预处理
+```
+测评数据都经过 全角转半角,繁简转化,标点符号标准化等操作;
+```
+
+### 1.3 其他说明
+```
+1.指标带common的极为宽松指标, 同开源项目pycorrector的评估指标;
+2.指标带strict的极为严格指标, 同开源项目[wangwang110/CSC](https://github.com/wangwang110/CSC);
+3.macbert4mdcspell_v1模型为训练使用mdcspell架构+bert的mlm-loss, 但是推理的时候只用bert-mlm;
+4.acc_rmrb/acc_xxqg数据集没有错误, 用于评估模型的误纠率(过度纠错);
+5.qwen25_1-5b_pycorrector的模型为shibing624/chinese-text-correction-1.5b, 其训练数据包括了lemon_v2/mcsc_tet/ecspell的验证集和测试集, 其他的bert类模型的训练不包括验证集和测试集;
+```
+
+
+## 二、重要指标
+### 2.1 F1(common_cor_f1)
+| model/common_cor_f1| avg| gen_de3| lemon_v2| gen_passage| text_proof| gen_xxqg| faspell| lomo_tet| mcsc_tet| ecspell| sighan2013| sighan2014| sighan2015 |
+|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|
+| macbert4csc_pycorrector| 45.8| 42.44| 42.89| 31.49| 46.31| 26.06| 32.7| 44.83| 27.93| 55.51| 70.89| 61.72| 66.81 |
+| bert4csc_v1| 62.28| 93.73| 61.99| 44.79| 68.0| 35.03| 48.28| 61.8| 64.41| 79.11| 77.66| 51.01| 61.54 |
+| macbert4csc_v1| 68.55| 96.67| 65.63| 48.4| 75.65| 38.43| 51.76| 70.11| 80.63| 85.55| 81.38| 57.63| 70.7 |
+| macbert4csc_v2| 68.6| 96.74| 66.02| 48.26| 75.78| 38.84| 51.91| 70.17| 80.71| 85.61| 80.97| 58.22| 69.95 |
+| macbert4mdcspell_v1| 71.1| 96.42| 70.06| 52.55| 79.61| 43.37| 53.85| 70.9| 82.38| 87.46| 84.2| 61.08| 71.32 |
+| qwen25_1-5b_pycorrector| 45.11| 27.29| 89.48| 14.61| 83.9| 13.84| 18.2| 36.71| 96.29| 88.2| 36.41| 15.64| 20.73 |
+
+### 2.2 acc(common_cor_acc)
+| model/common_cor_acc| avg| gen_de3| lemon_v2| gen_passage| text_proof| gen_xxqg| faspell| lomo_tet| mcsc_tet| ecspell| sighan2013| sighan2014| sighan2015 |
+|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|
+| macbert4csc_pycorrector| 48.26| 26.96| 28.68| 34.16| 55.29| 28.38| 22.2| 60.96| 57.16| 67.73| 55.9| 68.93| 72.73 |
+| bert4csc_v1| 60.76| 88.21| 45.96| 43.13| 68.97| 35.0| 34.0| 65.86| 73.26| 81.8| 64.5| 61.11| 67.27 |
+| macbert4csc_v1| 65.34| 93.56| 49.76| 44.98| 74.64| 36.1| 37.0| 73.0| 83.6| 86.87| 69.2| 62.62| 72.73 |
+| macbert4csc_v2| 65.22| 93.69| 50.14| 44.92| 74.64| 36.26| 37.0| 72.72| 83.66| 86.93| 68.5| 62.43| 71.73 |
+| macbert4mdcspell_v1| 67.15| 93.09| 54.8| 47.71| 78.09| 39.52| 38.8| 71.92| 84.78| 88.27| 73.2| 63.28| 72.36 |
+| qwen25_1-5b_pycorrector| 46.09| 15.82| 81.29| 22.96| 82.17| 19.04| 12.8| 50.2| 96.4| 89.13| 22.8| 27.87| 32.55 |
+
+### 2.3 acc(acc_true, thr=0.75)
+| model/acc                | avg| acc_rmrb| acc_xxqg |
+|:-------------------------|:-----------------|:-----------------|:-----------------|
+| macbert4csc_pycorrector  | 99.24| 99.22| 99.26 |
+| bert4csc_v1          | 98.71| 98.36| 99.06 |
+| macbert4csc_v1           | 97.72| 96.72| 98.72 |
+| macbert4csc_v2           | 97.89| 96.98| 98.8 |
+| macbert4mdcspell_v1      | 97.75| 96.51| 98.98 |
+| qwen25_1-5b_pycorrector  | 82.0| 77.14| 86.86 |
+
+## 二、结论(Conclusion)
+```
+1.macbert4csc_v1/macbert4csc_v2/macbert4mdcspell_v1等模型使用多种领域数据训练, 比较均衡, 也适合作为第一步的预训练模型, 可用于专有领域数据的继续微调;
+2.比较macbert4csc_pycorrector/bertbase4csc_v1/macbert4csc_v2/macbert4mdcspell_v1, 观察表2.3, 可以发现训练数据越多, 准确率提升的同时, 误纠率也会稍微高一些;
+3.MFT(Mask-Correct)依旧有效, 不过对于数据量足够的情形提升不明显, 可能也是误纠率升高的一个重要原因;
+4.训练数据中也存在文言文数据, 训练好的模型也支持文言文纠错;
+5.训练好的模型对"地得的"等高频错误具有较高的识别率和纠错率;
+```
+
+## 三、调用(Usage)
+### 3.1 使用macro-correct
+```
+from macro_correct import correct
+### 默认纠错(list输入)
+text_list = ["真麻烦你了。希望你们好好的跳无",
+             "少先队员因该为老人让坐",
+             "机七学习是人工智能领遇最能体现智能的一个分知",
+             "一只小鱼船浮在平净的河面上"
+             ]
+text_csc = correct(text_list)
+print("默认纠错(list输入):")
+print(text_csc)
+print("#" * 128)
+
+"""
+默认纠错(list输入):
+[
+{'index': 1, 'text': '真麻烦你了。希望你们好好的跳无。', 'target': '真麻烦你了。希望你们好好的跳舞。', 'errors': [['无', '舞', 14, 1.0]]}, 
+{'index': 2, 'text': '少先队员因该为老人让坐。', 'target': '少先队员应该为老人让坐。', 'errors': [['因', '应', 4, 0.9924]]}, 
+{'index': 3, 'text': '机七学习是人工智能领遇最能体现智能的一个分知。', 'target': '机器学习是人工智能领域最能体现智能的一个分支。', 'errors': [['知', '支', 21, 1.0], ['七', '器', 1, 1.0], ['遇', '域', 10, 1.0]]}, 
+{'index': 4, 'text': '一只小鱼船浮在平净的河面上。', 'target': '一只小鱼船浮在平静的河面上。', 'errors': [['净', '静', 8, 0.9981]]}]
+"""
+```
+
+### 3.2 使用 transformers
+```
+# !/usr/bin/python
+# -*- coding: utf-8 -*-
+# @time    : 2021/2/29 21:41
+# @author  : Mo
+# @function: transformers直接加载bert类模型测试
+
+
+import traceback
+import time
+import sys
+import os
+os.environ["USE_TORCH"] = "1"
+from transformers import BertConfig, BertTokenizer, BertForMaskedLM
+import torch
+
+# pretrained_model_name_or_path = "shibing624/macbert4csc-base-chinese"
+# pretrained_model_name_or_path = "Macropodus/macbert4mdcspell_v1"
+pretrained_model_name_or_path = "Macropodus/macbert4csc_v1"
+# pretrained_model_name_or_path = "Macropodus/macbert4csc_v2"
+# pretrained_model_name_or_path = "Macropodus/bert4csc_v1"
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+max_len = 128
+
+print("load model, please wait a few minute!")
+tokenizer = BertTokenizer.from_pretrained(pretrained_model_name_or_path)
+bert_config = BertConfig.from_pretrained(pretrained_model_name_or_path)
+model = BertForMaskedLM.from_pretrained(pretrained_model_name_or_path)
+model.to(device)
+print("load model success!")
+
+texts = [
+    "机七学习是人工智能领遇最能体现智能的一个分知",
+    "我是练习时长两念半的鸽仁练习生蔡徐坤",
+    "真麻烦你了。希望你们好好的跳无",
+    "他法语说的很好，的语也不错",
+    "遇到一位很棒的奴生跟我疗天",
+    "我们为这个目标努力不解",
+]
+len_mid = min(max_len, max([len(t)+2 for t in texts]))
+
+with torch.no_grad():
+    outputs = model(**tokenizer(texts, padding=True, max_length=len_mid,
+                                return_tensors="pt").to(device))
+
+def get_errors(source, target):
+    """   极简方法获取 errors   """
+    len_min = min(len(source), len(target))
+    errors = []
+    for idx in range(len_min):
+        if source[idx] != target[idx]:
+            errors.append([source[idx], target[idx], idx])
+    return errors
+
+result = []
+for probs, source in zip(outputs.logits, texts):
+    ids = torch.argmax(probs, dim=-1)
+    tokens_space = tokenizer.decode(ids[1:-1], skip_special_tokens=False)
+    text_new = tokens_space.replace(" ", "")
+    target = text_new[:len(source)]
+    errors = get_errors(source, target)
+    print(source, " => ", target, errors)
+    result.append([target, errors])
+print(result)
+"""
+机七学习是人工智能领遇最能体现智能的一个分知  =>  机器学习是人工智能领域最能体现智能的一个分支 [['七', '器', 1], ['遇', '域', 10], ['知', '支', 21]]
+我是练习时长两念半的鸽仁练习生蔡徐坤  =>  我是练习时长两年半的个人练习生蔡徐坤 [['念', '年', 7], ['鸽', '个', 10], ['仁', '人', 11]]
+真麻烦你了。希望你们好好的跳无  =>  真麻烦你了。希望你们好好地跳舞 [['的', '地', 12], ['无', '舞', 14]]
+他法语说的很好，的语也不错  =>  他法语说得很好，德语也不错 [['的', '得', 4], ['的', '德', 8]]
+遇到一位很棒的奴生跟我疗天  =>  遇到一位很棒的女生跟我聊天 [['奴', '女', 7], ['疗', '聊', 11]]
+我们为这个目标努力不解  =>  我们为这个目标努力不懈 [['解', '懈', 10]]
+"""
+```
+
+## 四、论文(Paper)
+ - 2024-Refining: [Refining Corpora from a Model Calibration Perspective for Chinese](https://arxiv.org/abs/2407.15498)
+ - 2024-ReLM: [Chinese Spelling Correction as Rephrasing Language Model](https://arxiv.org/abs/2308.08796)
+ - 2024-DICS: [DISC: Plug-and-Play Decoding Intervention with Similarity of Characters for Chinese Spelling Check](https://arxiv.org/abs/2412.12863)
+
+ - 2023-Bi-DCSpell: [A Bi-directional Detector-Corrector Interactive Framework for Chinese Spelling Check]()
+ - 2023-BERT-MFT: [Rethinking Masked Language Modeling for Chinese Spelling Correction](https://arxiv.org/abs/2305.17721)
+ - 2023-PTCSpell: [PTCSpell: Pre-trained Corrector Based on Character Shape and Pinyin for Chinese Spelling Correction](https://arxiv.org/abs/2212.04068)
+ - 2023-DR-CSC: [A Frustratingly Easy Plug-and-Play Detection-and-Reasoning Module for Chinese](https://aclanthology.org/2023.findings-emnlp.771)
+ - 2023-DROM: [Disentangled Phonetic Representation for Chinese Spelling Correction](https://arxiv.org/abs/2305.14783)
+ - 2023-EGCM: [An Error-Guided Correction Model for Chinese Spelling Error Correction](https://arxiv.org/abs/2301.06323)
+ - 2023-IGPI: [Investigating Glyph-Phonetic Information for Chinese Spell Checking: What Works and What’s Next?](https://arxiv.org/abs/2212.04068)
+ - 2023-CL: [Contextual Similarity is More Valuable than Character Similarity-An Empirical Study for Chinese Spell Checking]()
+
+ - 2022-CRASpell: [CRASpell: A Contextual Typo Robust Approach to Improve Chinese Spelling Correction](https://aclanthology.org/2022.findings-acl.237)
+ - 2022-MDCSpell: [MDCSpell: A Multi-task Detector-Corrector Framework for Chinese Spelling Correction](https://aclanthology.org/2022.findings-acl.98)
+ - 2022-SCOPE: [Improving Chinese Spelling Check by Character Pronunciation Prediction: The Effects of Adaptivity and Granularity](https://arxiv.org/abs/2210.10996)
+ - 2022-ECOPO: [The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking](https://arxiv.org/abs/2203.00991)
+
+ - 2021-MLMPhonetics: [Correcting Chinese Spelling Errors with Phonetic Pre-training](https://aclanthology.org/2021.findings-acl.198)
+ - 2021-ChineseBERT: [ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information](https://aclanthology.org/2021.acl-long.161/)
+ - 2021-BERTCrsGad: [Global Attention Decoder for Chinese Spelling Error Correction](https://aclanthology.org/2021.findings-acl.122)
+ - 2021-ThinkTwice: [Think Twice: A Post-Processing Approach for the Chinese Spelling Error Correction](https://www.mdpi.com/2076-3417/11/13/5832)
+ - 2021-PHMOSpell: [PHMOSpell: Phonological and Morphological Knowledge Guided Chinese Spelling Chec](https://aclanthology.org/2021.acl-long.464)
+ - 2021-SpellBERT: [SpellBERT: A Lightweight Pretrained Model for Chinese Spelling Check](https://aclanthology.org/2021.emnlp-main.287)
+ - 2021-TwoWays: [Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models](https://aclanthology.org/2021.acl-short.56)
+ - 2021-ReaLiSe: [Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking](https://arxiv.org/abs/2105.12306)
+ - 2021-DCSpell: [DCSpell: A Detector-Corrector Framework for Chinese Spelling Error Correction](https://dl.acm.org/doi/10.1145/3404835.3463050)
+ - 2021-PLOME: [PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction](https://aclanthology.org/2021.acl-long.233)
+ - 2021-DCN: [Dynamic Connected Networks for Chinese Spelling Check](https://aclanthology.org/2021.findings-acl.216/)
+
+ - 2020-SoftMaskBERT: [Spelling Error Correction with Soft-Masked BERT](https://arxiv.org/abs/2005.07421)
+ - 2020-SpellGCN: [SpellGCN：Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check](https://arxiv.org/abs/2004.14166)
+ - 2020-ChunkCSC: [Chunk-based Chinese Spelling Check with Global Optimization](https://aclanthology.org/2020.findings-emnlp.184)
+ - 2020-MacBERT: [Revisiting Pre-Trained Models for Chinese Natural Language Processing](https://arxiv.org/abs/2004.13922)
+
+ - 2019-FASPell: [FASPell: A Fast, Adaptable, Simple, Powerful Chinese Spell Checker Based On DAE-Decoder Paradigm](https://aclanthology.org/D19-5522)
+ - 2018-Hybrid: [A Hybrid Approach to Automatic Corpus Generation for Chinese Spelling Checking](https://aclanthology.org/D18-1273)
+
+ - 2015-Sighan15: [Introduction to SIGHAN 2015 Bake-off for Chinese Spelling Check](https://aclanthology.org/W15-3106/)
+ - 2014-Sighan14: [Overview of SIGHAN 2014 Bake-off for Chinese Spelling Check](https://aclanthology.org/W14-6820/)
+ - 2013-Sighan13: [Chinese Spelling Check Evaluation at SIGHAN Bake-off 2013](https://aclanthology.org/W13-4406/)
+
+## 五、参考(Refer)
+ - [nghuyong/Chinese-text-correction-papers](https://github.com/nghuyong/Chinese-text-correction-papers)
+ - [destwang/CTCResources](https://github.com/destwang/CTCResources)
+ - [wangwang110/CSC](https://github.com/wangwang110/CSC)
+ - [chinese-poetry/chinese-poetry](https://github.com/chinese-poetry/chinese-poetry)
+ - [chinese-poetry/huajianji](https://github.com/chinese-poetry/huajianji)
+ - [garychowcmu/daizhigev20](https://github.com/garychowcmu/daizhigev20)
+ - [yangjianxin1/Firefly](https://github.com/yangjianxin1/Firefly)
+ - [Macropodus/xuexiqiangguo_428w](https://huggingface.co/datasets/Macropodus/xuexiqiangguo_428w)
+ - [Macropodus/csc_clean_wang271k](https://huggingface.co/datasets/Macropodus/csc_clean_wang271k)
+ - [Macropodus/csc_eval_public](https://huggingface.co/datasets//Macropodus/csc_eval_public)
+ - [shibing624/pycorrector](https://github.com/shibing624/pycorrector)
+ - [iioSnail/MDCSpell_pytorch](https://github.com/iioSnail/MDCSpell_pytorch)
+ - [gingasan/lemon](https://github.com/gingasan/lemon)
+ - [Claude-Liu/ReLM](https://github.com/Claude-Liu/ReLM)
+
+
+## 六、引用(Cite)
+For citing this work, you can refer to the present GitHub project. For example, with BibTeX:
+```
+@software{macro-correct,
+    url = {https://github.com/yongzhuo/macro-correct},
+    author = {Yongzhuo Mo},
+    title = {macro-correct},
+    year = {2025}
+```